{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 线性可分支持向量机\n",
    "## 任务：观察C取值对决策边界的影响\n",
    "## 数据集：data/ex6data1.mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = sio.loadmat('./data/ex6data1.mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['__header__', '__version__', '__globals__', 'X', 'y'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((51, 2), (51, 1))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X,y = data['X'],data['y']\n",
    "X.shape,y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_data():\n",
    "    plt.scatter(X[:,0],X[:,1],c = y.flatten(), cmap ='jet')\n",
    "    plt.xlabel('x1')\n",
    "    plt.ylabel('y1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc1 = SVC(C=1,kernel='linear')\n",
    "svc1.fit(X,y.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0], dtype=uint8)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc1.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9803921568627451"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc1.score(X,y.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_boundary(model):\n",
    "    x_min,x_max = -0.5,4.5\n",
    "    y_min,y_max = 1.3,5\n",
    "    xx,yy = np.meshgrid(np.linspace(x_min,x_max,500),\n",
    "                       np.linspace(y_min,y_max,500))\n",
    "    z = model.predict(np.c_[xx.flatten(),yy.flatten()])\n",
    "    \n",
    "    zz = z.reshape(xx.shape)\n",
    "    plt.contour(xx,yy,zz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_boundary(svc1)\n",
    "plot_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc100 = SVC(C=100,kernel='linear')\n",
    "svc100.fit(X,y.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1], dtype=uint8)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc100.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc100.score(X,y.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_boundary(svc100)\n",
    "plot_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
